Transcription factor networks of oligodendrogliomas treated with adjuvant radiotherapy or observation inform prognosis

Revolutionary multi-omics analysis revealing prognostic signature of thyroid cancer and subsequent in vitro validation of SNAI1 in mediating thyroid cancer progression through EMT

Thyroid carcinoma (TC), the most commonly diagnosed malignancy of the endocrine system, has witnessed a significant rise in incidence over the past few decades. The integration of scRNA-seq with other sequencing approaches offers researchers a distinct perspective to explore mechanisms underlying TC progression. Therefore, it is crucial to develop a prognostic model for TC patients by utilizing a multi-omics approach. We acquired and processed transcriptomic data from the TCGA-THCA dataset, including mRNA expression profiles, lncRNA expression profiles, miRNA expression profiles, methylation chip data, gene mutation data, and clinical data. We constructed a tumor-related risk model using machine learning methods and developed a consensus machine learning-driven signature (CMLS) for accurate and stable prediction of TC patient outcomes. 2 strains of undifferentiated TC cell lines and 1 strain of PTC cell line were utilized for in vitro validation. mRNA, protein levels of hub genes, epithelial-mesenchymal transition (EMT)-associated phenotypes were detected by a series of in vitro experiments. We identified 3 molecular subtypes of TC based on integrated multi-omics clustering algorithms, which were associated with overall survival and displayed distinct molecular features. We developed a CMLS based on 28 hub genes to predict patient outcomes, and demonstrated that CMLS outperformed other prognostic models. TC patients of relatively lower CMLS score had significantly higher levels of T cells, B cells, and macrophages, indicating an immune-activated state. Fibroblasts were predominantly enriched in the high CMLS group, along with markers associated with immune suppression and evasion. We identified several drugs that could be suitable for patients with high CMLS, including Staurosporine_1034, Rapamycin_1084, gemcitabine, and topotecan. SNAI1 was elevated in both undifferentiated TC cell lines, comparing to PTC cells. Knockdown of SNAI1 reduced the cell proliferation and EMT phenotypes of undifferentiated TC cells. Our findings highlight the importance of multi-omics analysis in understanding the molecular subtypes and immune characteristics of TC, and provide a novel prognostic model and potential therapeutic targets for this disease. Moreover, we identified SNAI1 in mediating TC progression through EMT in vitro.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction.

METHOD

We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models.

RESULT

Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis.

CONCLUSION

The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

Immunological profiles of human oligodendrogliomas define two distinct molecular subtypes

BACKGROUND

Human oligodendroglioma presents as a heterogeneous disease, primarily characterized by the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion. Therapy development for this tumor is hindered by incomplete knowledge of somatic driving alterations and suboptimal disease classification. We herein aim to identify intrinsic molecular subtypes through integrated analysis of transcriptome, genome and methylome.

METHODS

137 oligodendroglioma patients from the Cancer Genome Atlas (TCGA) dataset were collected for unsupervised clustering analysis of immune gene expression profiles and comparative analysis of genome and methylome. Two independent datasets containing 218 patients were used for validation.

FINDINGS

We identified and independently validated two reproducible subtypes associated with distinct molecular characteristics and clinical outcomes. The proliferative subtype, named Oligo1, was characterized by more tumors of CNS WHO grade 3, as well as worse prognosis compared to the Oligo2 subtype. Besides the clinicopathologic features, Oligo1 exhibited enrichment of cell proliferation, regulation of cell cycle and Wnt signaling pathways, and significantly altered genes, such as EGFR, NOTCH1 and MET. In contrast, Oligo2, with favorable outcome, presented increased activation of immune response and metabolic process. Higher T cell/APC co-inhibition and inhibitory checkpoint levels were observed in Oligo2 tumors. Finally, multivariable analysis revealed our classification was an independent prognostic factor in oligodendrogliomas, and the robustness of these molecular subgroups was verified in the validation cohorts.

INTERPRETATION

This study provides further insights into patient stratification as well as presents opportunities for therapeutic development in human oligodendrogliomas.

FUNDING

The funders are listed in the Acknowledgement.

Grading of adult diffuse gliomas according to the 2021 WHO Classification of Tumors of the Central Nervous System

The grading of gliomas based on histological features has been a subject of debate for several decades. A consensus has not yet been reached because of technical limitations and inter-observer variations. While the traditional grading system has failed to stratify the risk of IDH-mutant astrocytoma, canonical histological and proliferative markers may be applicable to the risk stratification of IDH-wild-type astrocytoma. Numerous studies have examined molecular markers in order to obtain more clinically relevant information that will improve the risk stratification of gliomas. The CDKN2A/B homozygous deletion for IDH-mutant astrocytoma and the following three criteria for IDH-wild-type astrocytoma: the concurrent gain of whole chromosome 7 and loss of whole chromosome 10, TERT promoter mutations, and EGFR amplification, were identified as independent molecular markers of the worst clinical outcomes. Therefore, the 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System adopted these molecular markers into the revised grading criteria of IDH-mutant and -wild-type astrocytoma, respectively, as a grading system within tumor types. Of note, several recent studies have shown that some low-grade IDH-wild-type astrocytoma lacking both the molecular glioblastoma signature and genetic alterations typical of pediatric-type gliomas may demonstrate a relatively indolent clinical course, suggesting the existence of lower-grade adult IDH-wild-type astrocytoma. In terms of oligodendroglioma, IDH-mutant, and 1p/19q codeleted, consistent makers that predict poor outcomes have not yet been identified, and, thus, the current criteria have remained unchanged. Molecular testing to fulfill the revised WHO criteria is, however, not always available worldwide, and in that case, an integrated diagnosis combining all available complementary information is highly recommended. This review discusses controversial issues surrounding legacy grading systems and newly identified potential genetic markers of adult diffuse gliomas and provides perspectives on future grading systems.